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Research Slides
Carolyn R. Fallahi, Ph. D.
Defining Important Terms
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Hypotheses
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Null hypothesis
Alternative hypothesis
***Goal: to reject the Null hypothesis
Designing a research study
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Ask a question…. Can we answer this
question via a research study?
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Operationalizing the hypothesis
Stating the independent variables (IV)
Understanding the dependent variables (DV)
Control variables
Different types of research
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Case study: Freud
Naturalistic observation
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Problems with observation
Natural setting versus laboratory setting
Cross sectional study versus longitudinal
study
Survey and Interview Data
Different Types of Research
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Descriptive data
Correlational research
Experimental Research
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Hypothesis, IV, DV, CV
Research and Publication
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Institutional Approval
Informed Consent to Research
Offering Inducements for Research Participation
Deception in Research
Debriefing
Humane Care and Use of Animals in Research
Plagiarism
Correlation
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Correlation measures the relationship or
association between two variables.
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The value of correlation is from -1 to +1.
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-1 and +1 represent perfect negative and
positive relationships.
Correlation
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Examples: +.70 correlation between IQ and
SAT scores.
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-.70 correlation between severity of
Schizophrenic symptoms and level of
socialization.
Correlation
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Correlation is measured mathematically
Example: Schizerall versus Haldol.
Probability
Probability is something that we hear about
and use everyday.
 There is a 70% chance of rain!
 Probability of flipping a coin and getting
Heads = 50%.
 Probability is measured between 0 and 1.
 0 = for sure the event won’t happen.
 1 = 100% sure that it will happen.
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Probability
Probability will be measured with p-values.
 Like correlation, I will give you the p-value to
interpret.
 P < .50
 P < .05
 P < .01
 For purposes of this class, p < .05 or less, will
be statistically significantly different.
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Probability
For example, if you were looking at a study
that involved proportions:
 70/100 patients improved with drug 1 where
20/100 patients improved with placebo.
 We would use a z-test.
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Probability
In another scenario, 4 different populations.
 Men, women, old, young
 Chi Square.
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Probability
P-value is the probability or the likelihood of
the null hypothesis being true.
 If p-value is small, say .05, then it is very
unlikely that the null hypothesis is true.
 If p-value is .15 or high, there is a high
probability that the null hypothesis is true.
 In this scenario, we accept the null hypothesis
and reject the alternative.
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Class Example
Drug study – Improve ADHD.
 Comparing new drug versus old drug.
 We believe the new drug, Adderall, will be
significantly better than the old drug, Ritalin.
 Please state the Ho and Ha hypotheses.
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Class Example
Ho: Adderall = Ritalin
 But we don’t believe that, so:
 Ha: Adderall will decrease symptoms of Adhd
better than will Ritalin.
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Class Example
Interpret the two correlations.
 Adderall – rho = -.85
 Ritalin – rho = -.60
 We cannot tell just from looking at the
correlations which is more effective, therefore,
we need p-values.
 P< .04.
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